diff options
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java')
| -rw-r--r-- | src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java | 55 |
1 files changed, 36 insertions, 19 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java index c9443a9f..4f14f4ef 100644 --- a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java +++ b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java @@ -23,6 +23,7 @@ package de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster; along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
+import java.util.Collection;
import java.util.Iterator;
import org.apache.batik.util.SVGConstants;
@@ -32,6 +33,8 @@ import de.lmu.ifi.dbs.elki.data.Cluster; import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.MeanModel;
+import de.lmu.ifi.dbs.elki.data.model.MedoidModel;
+import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.result.HierarchicalResult;
import de.lmu.ifi.dbs.elki.result.Result;
@@ -59,6 +62,7 @@ import de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.AbstractScatter * @author Heidi Kolb
*
* @apiviz.has MeanModel oneway - - visualizes
+ * @apiviz.has MedoidModel oneway - - visualizes
*/
public class ClusterMeanVisualization extends AbstractScatterplotVisualization {
/**
@@ -84,7 +88,7 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization { /**
* Clustering to visualize.
*/
- Clustering<MeanModel<? extends NumberVector<?, ?>>> clustering;
+ Clustering<Model> clustering;
/**
* Draw stars
@@ -111,10 +115,23 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization { MarkerLibrary ml = context.getStyleLibrary().markers();
double marker_size = context.getStyleLibrary().getSize(StyleLibrary.MARKERPLOT);
- Iterator<Cluster<MeanModel<? extends NumberVector<?, ?>>>> ci = clustering.getAllClusters().iterator();
+ Iterator<Cluster<Model>> ci = clustering.getAllClusters().iterator();
for(int cnum = 0; ci.hasNext(); cnum++) {
- Cluster<MeanModel<? extends NumberVector<?, ?>>> clus = ci.next();
- double[] mean = proj.fastProjectDataToRenderSpace(clus.getModel().getMean());
+ Cluster<Model> clus = ci.next();
+ Model model = clus.getModel();
+ double[] mean;
+ if(model instanceof MeanModel) {
+ @SuppressWarnings("unchecked")
+ MeanModel<? extends NumberVector<?, ?>> mmodel = (MeanModel<? extends NumberVector<?, ?>>) model;
+ mean = proj.fastProjectDataToRenderSpace(mmodel.getMean());
+ }
+ else if(model instanceof MedoidModel) {
+ MedoidModel mmodel = (MedoidModel) model;
+ mean = proj.fastProjectDataToRenderSpace(rel.get(mmodel.getMedoid()));
+ }
+ else {
+ continue;
+ }
// add a greater Marker for the mean
Element meanMarker = ml.useMarker(svgp, layer, mean[0], mean[1], cnum, marker_size * 3);
@@ -163,7 +180,7 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization { if(stars) {
ColorLibrary colors = context.getStyleLibrary().getColorSet(StyleLibrary.PLOT);
- Iterator<Cluster<MeanModel<? extends NumberVector<?, ?>>>> ci = clustering.getAllClusters().iterator();
+ Iterator<Cluster<Model>> ci = clustering.getAllClusters().iterator();
for(int cnum = 0; ci.hasNext(); cnum++) {
ci.next();
if(!svgp.getCSSClassManager().contains(CSS_MEAN_STAR + "_" + cnum)) {
@@ -219,16 +236,13 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization { @Override
public void processNewResult(HierarchicalResult baseResult, Result result) {
// Find clusterings we can visualize:
- Iterator<Clustering<?>> clusterings = ResultUtil.filteredResults(result, Clustering.class);
- while(clusterings.hasNext()) {
- Clustering<?> c = clusterings.next();
+ Collection<Clustering<?>> clusterings = ResultUtil.filterResults(result, Clustering.class);
+ for(Clustering<?> c : clusterings) {
if(c.getAllClusters().size() > 0) {
// Does the cluster have a model with cluster means?
- Clustering<MeanModel<? extends NumberVector<?, ?>>> mcls = findMeanModel(c);
- if(mcls != null) {
- Iterator<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);
- while(ps.hasNext()) {
- ScatterPlotProjector<?> p = ps.next();
+ if(testMeanModel(c)) {
+ Collection<ScatterPlotProjector<?>> ps = ResultUtil.filterResults(baseResult, ScatterPlotProjector.class);
+ for(ScatterPlotProjector<?> p : ps) {
final VisualizationTask task = new VisualizationTask(NAME, c, p.getRelation(), this);
task.put(VisualizationTask.META_LEVEL, VisualizationTask.LEVEL_DATA + 1);
baseResult.getHierarchy().add(c, task);
@@ -243,14 +257,17 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization { * Test if the given clustering has a mean model.
*
* @param c Clustering to inspect
- * @return the clustering cast to return a mean model, null otherwise.
+ * @return true when the clustering has a mean or medoid model.
*/
- @SuppressWarnings("unchecked")
- private static Clustering<MeanModel<? extends NumberVector<?, ?>>> findMeanModel(Clustering<?> c) {
- if(c.getAllClusters().get(0).getModel() instanceof MeanModel<?>) {
- return (Clustering<MeanModel<? extends NumberVector<?, ?>>>) c;
+ private static boolean testMeanModel(Clustering<?> c) {
+ Model firstmodel = c.getAllClusters().get(0).getModel();
+ if(firstmodel instanceof MeanModel<?>) {
+ return true;
+ }
+ if(firstmodel instanceof MedoidModel) {
+ return true;
}
- return null;
+ return false;
}
/**
|